scLANE Simulation Study - Trajectory DE Method Comparison

UF Dept. of Biostatistics - Bacher Group

Libraries

library(dplyr)
library(ggplot2)
library(targets)

Helper Functions

source("R/functions_analysis.R")

Data

tar_load(metric_table_master)

Analysis

Reference Dataset

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = ROC_AUC, y = SIM_REFERENCE, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  facet_wrap(~MODEL_TYPE) + 
  ggridges::geom_density_ridges(alpha = 0.6, scale = 0.95, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_percent()) + 
  labs(y = "Reference Dataset", x = "ROC-AUC") + 
  theme_analysis() + 
  theme(legend.position = "none")

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = F_MEASURE, y = SIM_REFERENCE, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  facet_wrap(~MODEL_TYPE) + 
  ggridges::geom_density_ridges(alpha = 0.6, scale = 0.95, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_number(accuracy = 0.1), 
                     limits = c(NA, 1)) + 
  labs(y = "Reference Dataset", x = "F-measure") + 
  theme_analysis() + 
  theme(legend.position = "none")

Runtime

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS), 
         RUNTIME_MINS = RUNTIME_HOURS * 60) %>% 
  ggplot(aes(x = N_CELLS, y = RUNTIME_MINS, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_number(accuracy = 1, suffix = "min")) + 
  labs(x = "Cells", y = "Runtime") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(RUNTIME_MINS = RUNTIME_HOURS * 60, 
         GENES_PER_MIN = N_GENES / RUNTIME_MINS) %>% 
  ggplot(aes(x = N_CELLS, y = GENES_PER_MIN, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_smooth(method = "lm", alpha = 0.25) + 
  scale_y_continuous(labels = scales::label_number(accuracy = 1)) + 
  labs(x = "Cells", y = "Genes per Minute") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 0, linewidth = 2)))

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(RUNTIME_MINS = RUNTIME_HOURS * 60, 
         GENES_PER_MIN = N_GENES / RUNTIME_MINS) %>% 
  ggplot(aes(x = GENES_PER_MIN, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_number(accuracy = 1)) + 
  labs(x = "Genes per Minute", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

Memory Usage

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS), 
         MEM_USED_GB = MEM_USED / 1000) %>% 
  ggplot(aes(x = N_CELLS, y = MEM_USED_GB, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_number(suffix = "gb")) + 
  labs(x = "Cells", y = "Memory Usage") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(MEM_USED_GB = MEM_USED / 1000) %>% 
  ggplot(aes(x = MEM_USED_GB, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_number(suffix = "gb")) + 
  labs(x = "Memory Usage", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

Predictive Performance

F-measure

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = N_CELLS, y = F_MEASURE, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_number(accuracy = 0.1)) + 
  labs(x = "Cells", y = "F-measure") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = F_MEASURE, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_number(accuracy = 0.1)) + 
  labs(x = "F-measure", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

Balanced Accuracy

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = N_CELLS, y = BAL_ACCURACY, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Cells", y = "Balanced Accuracy") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = BAL_ACCURACY, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Balanced Accuracy", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

Recall

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = N_CELLS, y = RECALL, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Cells", y = "Recall") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = RECALL, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Recall", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

Accuracy

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = N_CELLS, y = ACCURACY)) + 
  geom_boxplot(aes(color = MODEL_TYPE)) + 
  scale_y_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Cells", y = "Accuracy") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = ACCURACY, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Accuracy", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

ROC-AUC

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = N_CELLS, y = ROC_AUC, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Cells", y = "ROC-AUC") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = ROC_AUC, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "ROC-AUC", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM")) %>% 
  pull(ROC_CURVE) %>% 
  purrr::reduce(rbind) %>% 
  left_join((distinct(metric_table_master, DATASET_NAME, N_CELLS)), 
            by = c("dataset" = "DATASET_NAME")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = 1 - specificity, y = sensitivity, group = dataset, color = N_CELLS)) + 
  facet_wrap(~paste0("Cells: ", N_CELLS)) + 
  geom_segment(x = 0, xend = 0, y = 1, yend = 1, color = "black", linetype = "dashed", size = 1) + 
  geom_line(size = 1, alpha = 0.8) + 
  scale_x_continuous(labels = scales::percent_format(accuracy = 1)) + 
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) + 
  labs(x = "1 - Specificity", 
      y = "Sensitivity", 
      color = "Cells", 
      title = "scLANE - GLM") + 
  theme_analysis() +  
  guides(color = guide_legend(override.aes = list(linewidth = 2, alpha = 1)))

filter(metric_table_master, 
       MODEL_TYPE %in% c("tradeSeq")) %>% 
  pull(ROC_CURVE) %>% 
  purrr::reduce(rbind) %>% 
  left_join((distinct(metric_table_master, DATASET_NAME, N_CELLS)), 
            by = c("dataset" = "DATASET_NAME")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = 1 - specificity, y = sensitivity, group = dataset, color = N_CELLS)) + 
  facet_wrap(~paste0("Cells: ", N_CELLS)) + 
  geom_segment(x = 0, xend = 0, y = 1, yend = 1, color = "black", linetype = "dashed", size = 1) + 
  geom_line(size = 1, alpha = 0.8) + 
  scale_x_continuous(labels = scales::percent_format(accuracy = 1)) + 
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) + 
  labs(x = "1 - Specificity", 
      y = "Sensitivity", 
      color = "Cells", 
      title = "tradeSeq") + 
  theme_analysis() + 
  guides(color = guide_legend(override.aes = list(linewidth = 2, alpha = 1)))

PR-AUC

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = N_CELLS, y = AUC_PR, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Cells", y = "PR-AUC") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = AUC_PR, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "PR-AUC", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM")) %>% 
  pull(PR_CURVE) %>% 
  purrr::reduce(rbind) %>% 
  left_join((distinct(metric_table_master, DATASET_NAME, N_CELLS)), 
            by = c("dataset" = "DATASET_NAME")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = recall, y = precision, group = dataset, color = N_CELLS)) + 
  facet_wrap(~paste0("Cells: ", N_CELLS)) + 
  geom_segment(x = 0, xend = 1, y = 1, yend = 0, color = "black", linetype = "dashed", size = 1) + 
  geom_line(size = 1, alpha = 0.8) + 
  scale_x_continuous(labels = scales::percent_format(accuracy = 1)) +
  scale_y_continuous(limits = c(0, 1), labels = scales::percent_format(accuracy = 1)) +
  labs(x = "Recall", 
       y = "Precision", 
       color = "Cells", 
       title = "scLANE - GLM") + 
  theme_analysis() + 
  guides(color = guide_legend(override.aes = list(linewidth = 2, alpha = 1)))

filter(metric_table_master, 
       MODEL_TYPE %in% c("tradeSeq")) %>% 
  pull(PR_CURVE) %>% 
  purrr::reduce(rbind) %>% 
  left_join((distinct(metric_table_master, DATASET_NAME, N_CELLS)), 
            by = c("dataset" = "DATASET_NAME")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = recall, y = precision, group = dataset, color = N_CELLS)) + 
  facet_wrap(~paste0("Cells: ", N_CELLS)) + 
  geom_segment(x = 0, xend = 1, y = 1, yend = 0, color = "black", linetype = "dashed", size = 1) + 
  geom_line(size = 1, alpha = 0.8) + 
  scale_x_continuous(labels = scales::percent_format(accuracy = 1)) +
  scale_y_continuous(limits = c(0, 1), labels = scales::percent_format(accuracy = 1)) +
  labs(x = "Recall", 
       y = "Precision", 
       color = "Cells", 
       title = "tradeSeq") + 
  theme_analysis() + 
  guides(color = guide_legend(override.aes = list(linewidth = 2, alpha = 1)))

Session Info

sessioninfo::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.2.3 (2023-03-15)
 os       Ubuntu 22.04.3 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/New_York
 date     2024-04-21
 pandoc   2.9.2.1 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package              * version    date (UTC) lib source
 AnnotationDbi          1.60.2     2023-03-10 [2] Bioconductor
 backports              1.4.1      2021-12-13 [2] CRAN (R 4.2.0)
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 bigassertr             0.1.6      2023-01-10 [1] CRAN (R 4.3.2)
 bigparallelr           0.3.2      2021-10-02 [1] CRAN (R 4.3.2)
 bigstatsr              1.5.12     2022-10-14 [1] CRAN (R 4.3.2)
 Biobase              * 2.58.0     2022-11-01 [2] Bioconductor
 BiocGenerics         * 0.44.0     2023-02-01 [2] bioc_git2r (@d7cd9c1)
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 Biostrings             2.66.0     2022-11-01 [2] Bioconductor
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 blob                   1.2.4      2023-03-17 [2] CRAN (R 4.2.3)
 boot                   1.3-28.1   2022-11-22 [2] CRAN (R 4.2.2)
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 future               * 1.33.2     2024-03-26 [1] CRAN (R 4.2.3)
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 future.callr         * 0.8.2      2023-08-09 [1] CRAN (R 4.2.3)
 gamlss                 5.4-22     2024-03-20 [1] CRAN (R 4.3.2)
 gamlss.data            6.0-6      2024-03-14 [1] CRAN (R 4.3.2)
 gamlss.dist            6.1-1      2023-08-23 [1] CRAN (R 4.3.2)
 geeM                   0.10.1     2018-06-18 [1] CRAN (R 4.3.2)
 generics               0.1.3      2022-07-05 [1] CRAN (R 4.2.0)
 GenomeInfoDb         * 1.34.9     2023-02-02 [2] Bioconductor
 GenomeInfoDbData       1.2.9      2023-01-19 [2] Bioconductor
 GenomicRanges        * 1.50.2     2022-12-16 [2] Bioconductor
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 ggplot2              * 3.5.0      2024-02-23 [1] CRAN (R 4.2.3)
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 glm2                 * 1.2.1      2018-08-11 [1] CRAN (R 4.3.2)
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 htmltools              0.5.8.1    2024-04-04 [1] CRAN (R 4.2.3)
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 httr                   1.4.7      2023-08-15 [2] CRAN (R 4.2.3)
 igraph                 2.0.3      2024-03-13 [1] CRAN (R 4.2.3)
 infer                * 1.0.7      2024-03-25 [1] CRAN (R 4.2.3)
 ipred                  0.9-14     2023-03-09 [2] CRAN (R 4.2.3)
 IRanges              * 2.32.0     2022-11-01 [2] Bioconductor
 iterators              1.0.14     2022-02-05 [2] CRAN (R 4.2.0)
 jquerylib              0.1.4      2021-04-26 [2] CRAN (R 4.2.0)
 jsonlite               1.8.8      2023-12-04 [1] CRAN (R 4.2.3)
 KEGGREST               1.38.0     2022-11-01 [2] Bioconductor
 KernSmooth             2.23-22    2023-07-10 [4] CRAN (R 4.2.3)
 knitr                  1.44       2023-09-11 [2] CRAN (R 4.2.3)
 labeling               0.4.3      2023-08-29 [2] CRAN (R 4.2.3)
 Lamian               * 0.99.2     2024-03-20 [1] Github (Winnie09/Lamian@7d5a8ff)
 later                  1.3.1      2023-05-02 [2] CRAN (R 4.2.3)
 lattice                0.21-8     2023-04-05 [4] CRAN (R 4.2.3)
 lava                   1.7.2.1    2023-02-27 [2] CRAN (R 4.2.3)
 lhs                    1.1.6      2022-12-17 [2] CRAN (R 4.2.2)
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 limma                  3.54.2     2023-02-28 [2] Bioconductor
 listenv                0.9.0      2022-12-16 [2] CRAN (R 4.2.2)
 lme4                   1.1-34     2023-07-04 [2] CRAN (R 4.2.3)
 locfit                 1.5-9.8    2023-06-11 [2] CRAN (R 4.2.3)
 lubridate            * 1.9.3      2023-09-27 [2] CRAN (R 4.2.3)
 magrittr             * 2.0.3      2022-03-30 [1] CRAN (R 4.2.0)
 MASS                   7.3-60     2023-05-04 [4] CRAN (R 4.2.3)
 Matrix                 1.6-5      2024-01-11 [1] CRAN (R 4.2.3)
 matrixcalc             1.0-6      2022-09-14 [1] CRAN (R 4.3.2)
 MatrixGenerics       * 1.10.0     2022-11-01 [2] Bioconductor
 matrixStats          * 1.0.0      2023-06-02 [2] CRAN (R 4.2.3)
 mclust                 6.0.0      2022-10-31 [2] CRAN (R 4.2.1)
 memoise                2.0.1      2021-11-26 [2] CRAN (R 4.2.0)
 mgcv                   1.9-0      2023-07-11 [4] CRAN (R 4.2.3)
 mime                   0.12       2021-09-28 [2] CRAN (R 4.2.0)
 miniUI                 0.1.1.1    2018-05-18 [2] CRAN (R 4.2.0)
 minqa                  1.2.6      2023-09-11 [2] CRAN (R 4.2.3)
 modeldata            * 1.3.0      2024-01-21 [1] CRAN (R 4.2.3)
 multcomp               1.4-25     2023-06-20 [2] CRAN (R 4.2.3)
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 mvtnorm                1.2-3      2023-08-25 [2] CRAN (R 4.2.3)
 nanonext               0.13.5.2   2024-04-06 [1] CRAN (R 4.2.3)
 nlme                   3.1-163    2023-08-09 [2] CRAN (R 4.2.3)
 nloptr                 2.0.3      2022-05-26 [2] CRAN (R 4.2.0)
 nnet                   7.3-19     2023-05-03 [1] CRAN (R 4.2.3)
 numDeriv               2016.8-1.1 2019-06-06 [2] CRAN (R 4.2.0)
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 parallelly             1.37.1     2024-02-29 [1] CRAN (R 4.2.3)
 parsnip              * 1.2.1      2024-03-22 [1] CRAN (R 4.2.3)
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 plyr                   1.8.9      2023-10-02 [2] CRAN (R 4.2.3)
 png                    0.1-8      2022-11-29 [2] CRAN (R 4.2.2)
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 processx               3.8.2      2023-06-30 [2] CRAN (R 4.2.3)
 prodlim                2023.08.28 2023-08-28 [2] CRAN (R 4.2.3)
 profvis                0.3.8      2023-05-02 [2] CRAN (R 4.2.3)
 promises               1.2.1      2023-08-10 [2] CRAN (R 4.2.3)
 ps                     1.7.5      2023-04-18 [2] CRAN (R 4.2.3)
 purrr                * 1.0.2      2023-08-10 [1] CRAN (R 4.2.3)
 qs                   * 0.26.1     2024-03-09 [1] CRAN (R 4.3.2)
 R6                     2.5.1      2021-08-19 [2] CRAN (R 4.2.0)
 RApiSerialize          0.1.2      2022-08-25 [1] CRAN (R 4.2.0)
 RColorBrewer           1.1-3      2022-04-03 [2] CRAN (R 4.2.0)
 Rcpp                   1.0.11     2023-07-06 [2] CRAN (R 4.2.3)
 RcppEigen              0.3.4.0.0  2024-02-28 [1] CRAN (R 4.2.3)
 RcppParallel           5.1.7      2023-02-27 [1] CRAN (R 4.3.2)
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 readr                * 2.1.4      2023-02-10 [2] CRAN (R 4.2.3)
 recipes              * 1.0.10     2024-02-18 [1] CRAN (R 4.2.3)
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 reshape2               1.4.4      2020-04-09 [2] CRAN (R 4.2.0)
 rhdf5                  2.42.1     2023-04-07 [2] Bioconductor
 rhdf5filters           1.10.1     2023-03-24 [2] Bioconductor
 Rhdf5lib               1.20.0     2022-11-01 [2] Bioconductor
 rjson                  0.2.21     2022-01-09 [2] CRAN (R 4.2.0)
 rlang                  1.1.3      2024-01-10 [1] CRAN (R 4.2.3)
 rmarkdown            * 2.26       2024-03-05 [1] CRAN (R 4.2.3)
 rpart                  4.1.21     2023-10-09 [4] CRAN (R 4.2.3)
 rsample              * 1.2.1      2024-03-25 [1] CRAN (R 4.2.3)
 RSQLite                2.3.1      2023-04-03 [2] CRAN (R 4.2.3)
 rstudioapi             0.16.0     2024-03-24 [1] CRAN (R 4.2.3)
 S4Vectors            * 0.36.2     2023-02-26 [2] Bioconductor
 sandwich               3.1-0      2023-12-11 [1] CRAN (R 4.2.3)
 sass                   0.4.9      2024-03-15 [1] CRAN (R 4.2.3)
 scales               * 1.3.0      2023-11-28 [1] CRAN (R 4.2.3)
 scattermore            1.2        2023-06-12 [2] CRAN (R 4.2.3)
 scLANE               * 0.7.9      2024-04-07 [1] Github (jr-leary7/scLANE@d4ad28a)
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 sessioninfo            1.2.2      2021-12-06 [2] CRAN (R 4.2.0)
 shape                  1.4.6      2021-05-19 [2] CRAN (R 4.2.0)
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 SparseM                1.81       2021-02-18 [2] CRAN (R 4.2.0)
 stringfish             0.16.0     2023-11-28 [1] CRAN (R 4.2.3)
 stringi                1.7.12     2023-01-11 [2] CRAN (R 4.2.2)
 stringr              * 1.5.1      2023-11-14 [2] CRAN (R 4.2.3)
 SummarizedExperiment * 1.28.0     2023-02-01 [2] bioc_git2r (@ba55dac)
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 tarchetypes          * 0.8.0      2024-03-18 [1] CRAN (R 4.2.3)
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 TH.data                1.1-2      2023-04-17 [2] CRAN (R 4.2.3)
 tibble               * 3.2.1      2023-03-20 [2] CRAN (R 4.2.3)
 tidymodels           * 1.2.0      2024-03-25 [1] CRAN (R 4.2.3)
 tidyr                * 1.3.1      2024-01-24 [1] CRAN (R 4.2.3)
 tidyselect             1.2.0      2022-10-10 [2] CRAN (R 4.2.1)
 tidyverse            * 2.0.0      2023-02-22 [1] CRAN (R 4.2.3)
 timechange             0.2.0      2023-01-11 [2] CRAN (R 4.2.2)
 timeDate               4022.108   2023-01-07 [2] CRAN (R 4.2.2)
 TMB                    1.9.10     2023-12-12 [1] CRAN (R 4.2.3)
 topGO                  2.50.0     2022-11-01 [2] Bioconductor
 tradeSeq             * 1.12.0     2022-11-01 [1] Bioconductor
 TrajectoryUtils        1.6.0      2022-11-01 [1] Bioconductor
 TSCAN                  1.36.0     2022-11-01 [1] Bioconductor
 tune                 * 1.2.0      2024-03-20 [1] CRAN (R 4.2.3)
 tzdb                   0.4.0      2023-05-12 [2] CRAN (R 4.2.3)
 urlchecker             1.0.1      2021-11-30 [2] CRAN (R 4.2.0)
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 utf8                   1.2.3      2023-01-31 [2] CRAN (R 4.2.2)
 vctrs                  0.6.4      2023-10-12 [2] CRAN (R 4.2.3)
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 workflowsets         * 1.1.0      2024-03-21 [1] CRAN (R 4.2.3)
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 xtable                 1.8-4      2019-04-21 [2] CRAN (R 4.2.0)
 XVector                0.38.0     2022-11-01 [2] Bioconductor
 yaml                   2.3.7      2023-01-23 [2] CRAN (R 4.2.2)
 yardstick            * 1.3.1      2024-03-21 [1] CRAN (R 4.2.3)
 zlibbioc               1.44.0     2022-11-01 [2] Bioconductor
 zoo                    1.8-12     2023-04-13 [2] CRAN (R 4.2.3)

 [1] /home/j.leary/r_packages_default
 [2] /usr/local/lib/R/site-library
 [3] /usr/lib/R/site-library
 [4] /usr/lib/R/library

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